Standards Covered: ISA 5.1 | ISO 10628-2 | ISO 14617 | PIP PIC001
Compiled: March 2026
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ChintanTalapara
/ PID_Symbol_Library_Complete_Reference.md
Created
March 19, 2026 02:14
Complete P&ID Symbol Library & Drafting Protocols Reference - ISA 5.1, ISO 10628-2, Open-Source SVG Libraries, Tag Naming, Line Designation
| name | extract-clothing-cutouts |
|---|---|
| description | Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory. |
Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.
HarryAnkers
/ GUIDE.md
Last active
July 15, 2026 01:11
NVIDIA Virtual Display for Sunshine/Moonlight on Linux — No Dummy Plug Required (4K@120Hz, HDR, Custom Resolutions)
A guide for creating a virtual display on an NVIDIA GPU (tested on RTX 5080, driver 595.58) with HDR, custom resolutions, and 4K@120Hz support for headless Sunshine/Moonlight streaming on Linux.
Works on both HDMI and DisplayPort connectors with no physical display or dummy plug connected.
Running Sunshine headless on Linux with NVIDIA is painful:
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
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| /** | |
| * HttpBaseClient — a self-contained, framework-agnostic abstract HTTP client. | |
| * | |
| * Design goals: | |
| * - Single file, zero dependencies (Node 18+ / browser). | |
| * - Every outbound HTTP call goes through one pipeline so you get | |
| * consistent logging, error mapping, metrics, and query/body | |
| * serialization for free. | |
| * - Subclasses focus on vendor specifics: base URL, auth headers, | |
| * and optional error-body translation. |
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| $ ffmpeg -i input.mp4 -vcodec h264 -acodec mp2 output.mp4 |
mizchi
/ 技術記事、専門家としてのプログラマ、言語化.md
Created
June 20, 2026 05:55
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